Unmanned Aerial Vehicles (UAVs) have become indispensable assets in various sectors, leveraging their mobility and data collection capabilities. However, privacy and security concerns have fueled interest in Federated Learning (FL) as a solution. FL, decentralized and collaborative, offers promise in addressing privacy risks inherent in centralized data processing while enhancing model performance. In this review, we explore FL’s privacy and security implications in UAV ecosystems. We highlight FL’s potential to mitigate privacy risks by aggregating model updates locally, minimizing data transmission needs. Additionally, we examine security challenges and evaluate protective mechanisms. Through a systematic literature review, we identify gaps and propose future research directions, aiming to enhance the security and privacy of FL in UAV applications.